Overview¶

The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.

Built on top of the Plotly JavaScript library (plotly.js), plotly enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash. The plotly Python library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library.

Thanks to deep integration with our Kaleido image export utility, plotly also provides great support for non-web contexts including desktop editors (e.g. QtConsole, Spyder, PyCharm) and static document publishing (e.g. exporting notebooks to PDF with high-quality vector images).

This Getting Started guide explains how to install plotly and related optional pages. Once you've installed, you can use our documentation in three main ways:

You jump right in to examples of how to make basic charts, statistical charts, scientific charts, financial charts, maps, and 3-dimensional charts.
If you prefer to learn about the fundamentals of the library first, you can read about the structure of figures, how to create and update figures, how to display figures, how to theme figures with templates, how to export figures to various formats and about Plotly Express, the high-level API for doing all of the above.
You can check out our exhaustive reference guides: the Python API reference or the Figure Reference

For information on using Python to build web applications containing plotly figures, see the Dash User Guide.

We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.

Installation¶

plotly may be installed using pip:

$ pip install plotly

or conda:

$ conda install -c plotly plotly

If you want to use Plotly Express, install its required dependencies with:

pip install plotly[express]

You'll also need to install a supported dataframe library.

Plotly charts in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

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JupyterLab Support¶

To use plotly in JupyterLab, install the jupyterlab and anywidget packages in the same environment as you installed plotly, using pip:

$ pip install jupyterlab anywidget

or conda:

$ conda install jupyterlab anywidget

Launch JupyterLab with:

$ jupyter lab

and display plotly figures inline:

In [2]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])
fig.show()

or using FigureWidget objects.

In [3]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])

import plotly.graph_objects as go
fig_widget = go.FigureWidget(fig)
fig_widget

Out[3]:

See Displaying Figures in Python for more information on the renderers framework, and see Plotly FigureWidget Overview for more information on using FigureWidget.

See the Troubleshooting guide if you run into any problems with JupyterLab, particularly if you are using multiple Python environments inside Jupyter.

Jupyter Notebook Support¶

For use in the classic Jupyter Notebook, install the notebook and ipywidgets packages using pip:

pip install "notebook>=7.0" "anywidget>=0.9.13"

or conda:

conda install "notebook>=7.0" "anywidget>=0.9.13"

These packages contain everything you need to run a Jupyter notebook...

$ jupyter notebook

and display plotly figures inline using the notebook renderer...

In [4]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])
fig.show()

or using FigureWidget objects.

In [5]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])

import plotly.graph_objects as go
fig_widget = go.FigureWidget(fig)
fig_widget

Out[5]:

See Displaying Figures in Python for more information on the renderers framework, and see Plotly FigureWidget Overview for more information on using FigureWidget.

Static Image Export¶

plotly.py supports static image export, using the either the kaleido package (recommended, supported as of plotly version 4.9) or the orca command line utility (legacy as of plotly version 4.9).

Kaleido¶

The kaleido package has no dependencies and can be installed using pip...

$ pip install -U kaleido

or conda.

$ conda install -c plotly python-kaleido

Orca¶

While Kaleido is now the recommended image export approach because it is easier to install and more widely compatible, static image export can also be supported by the legacy orca command line utility and the psutil Python package.

These dependencies can both be installed using conda:

conda install -c plotly plotly-orca==1.3.1 psutil

Or, psutil can be installed using pip...

pip install psutil

and orca can be installed according to the instructions in the orca README.

Extended Geo Support¶

Some plotly.py features rely on fairly large geographic shape files. The county choropleth figure factory is one such example. These shape files are distributed as a separate plotly-geo package. This package can be installed using pip...

$ pip install plotly-geo==1.0.0

or conda.

$ conda install -c plotly plotly-geo=1.0.0

See USA County Choropleth Maps in Python for more information on the county choropleth figure factory.

Where to next?¶

Once you've installed, you can use our documentation in three main ways:

You jump right in to examples of how to make basic charts, statistical charts, scientific charts, financial charts, maps, and 3-dimensional charts.
If you prefer to learn about the fundamentals of the library first, you can read about the structure of figures, how to create and update figures, how to display figures, how to theme figures with templates, how to export figures to various formats and about Plotly Express, the high-level API for doing all of the above.
You can check out our exhaustive reference guides: the Python API reference or the Figure Reference

For information on using Python to build web applications containing plotly figures, see the Dash User Guide.

We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter